PCA analysis w 38
Goal: Do a PCA plot using Irene’s SNPs to look for relatedness among plants.
This version keeps sample 38LTR.
See “PCA analysis.Rmd” for info on SNP calling, etc.
library(tidyverse)[30m── [1mAttaching packages[22m ──────────────────────────────────────── tidyverse 1.2.1 ──[39m
[30m[32m✔[30m [34mggplot2[30m 3.0.0 [32m✔[30m [34mpurrr [30m 0.2.5
[32m✔[30m [34mtibble [30m 1.4.2 [32m✔[30m [34mdplyr [30m 0.7.6
[32m✔[30m [34mtidyr [30m 0.8.1 [32m✔[30m [34mstringr[30m 1.3.1
[32m✔[30m [34mreadr [30m 1.1.1 [32m✔[30m [34mforcats[30m 0.3.0[39m
[30m── [1mConflicts[22m ─────────────────────────────────────────── tidyverse_conflicts() ──
[31m✖[30m [34mdplyr[30m::[32mfilter()[30m masks [34mstats[30m::filter()
[31m✖[30m [34mdplyr[30m::[32mlag()[30m masks [34mstats[30m::lag()[39mlibrary(ggrepel)get the vcf header
vcf.header <- system("zgrep '#C' ../input/LT.vcf.gz",intern = TRUE)
vcf.header[1] "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t38LTR\t42LTR\t42LTRR\t43LTR\t43LTRR\t49LTWR\t49LTWRR\t95LTWR\t95LTWRR\t99LTWR"vcf.header <- vcf.header %>%
str_replace("#","") %>% #get rid of the pound sign
str_split(pattern = "\t") %>% #split on the tabs
magrittr::extract2(1)
vcf.header [1] "CHROM" "POS" "ID" "REF" "ALT" "QUAL" "FILTER"
[8] "INFO" "FORMAT" "38LTR" "42LTR" "42LTRR" "43LTR" "43LTRR"
[15] "49LTWR" "49LTWRR" "95LTWR" "95LTWRR" "99LTWR" get the data
snps <- read_tsv("../input/LT.vcf.gz", na = c("","NA","."),comment="#",col_names = vcf.header) %>%
select(-ID, -FILTER) # these are empty columnsParsed with column specification:
cols(
CHROM = col_character(),
POS = col_integer(),
ID = col_character(),
REF = col_character(),
ALT = col_character(),
QUAL = col_double(),
FILTER = col_character(),
INFO = col_character(),
FORMAT = col_character(),
`38LTR` = col_character(),
`42LTR` = col_character(),
`42LTRR` = col_character(),
`43LTR` = col_character(),
`43LTRR` = col_character(),
`49LTWR` = col_character(),
`49LTWRR` = col_character(),
`95LTWR` = col_character(),
`95LTWRR` = col_character(),
`99LTWR` = col_character()
)
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|==================================================================| 100% 398 MBsnpsfilter to keep snps where there is data from all samples
snps <- snps %>%
filter({select(., matches("[0-9]")) %>% complete.cases() })
snpssnps <- snps %>%
mutate(TOTAL_DEPTH= {str_extract(INFO, "DP=[0-9]*") %>%
str_remove("DP=") %>%
as.numeric() }
) %>%
filter(QUAL >=100,
nchar(ALT)==1,
TOTAL_DEPTH > quantile(TOTAL_DEPTH, 0.05),
TOTAL_DEPTH < quantile(TOTAL_DEPTH, 0.95))
snpsunpack the information differnet samples:
samples <- colnames(snps) %>% str_subset("^[0-9]")
for (s in samples) {
snps <- snps %>%
separate(!!s, into=paste(s,c("gt","tot.depth","allele.depth","ref.depth","ref.qual","alt.depth","alt.qual","gt.lik"),sep="_"),
sep=":", convert = TRUE)
}
snpsFor the PCA we only need the genotype info
gts <- snps %>%
select(CHROM, POS, ends_with("_gt"))
gtsconvert this to numeric
geno.numeric <- gts %>%
select(-CHROM, -POS) %>%
lapply(factor) %>% # convert charcters to "factors", where each category is internally represented as a number
as.data.frame() %>% # reformat
data.matrix() %>%# convert to numeric
t()
colnames(geno.numeric) <- str_c(gts$CHROM, "_", gts$POS)
head(geno.numeric[,1:5],10) GCZN01000007.1_2815 GCZN01000007.1_2834 GCZN01000007.1_2865
X38LTR_gt 2 2 2
X42LTR_gt 2 3 2
X42LTRR_gt 2 2 2
X43LTR_gt 2 2 2
X43LTRR_gt 2 2 2
X49LTWR_gt 2 2 2
X49LTWRR_gt 3 3 3
X95LTWR_gt 2 3 3
X95LTWRR_gt 2 3 3
X99LTWR_gt 2 2 2
GCZN01000007.1_2881 GCZN01000007.1_2970
X38LTR_gt 2 2
X42LTR_gt 2 2
X42LTRR_gt 2 1
X43LTR_gt 2 2
X43LTRR_gt 2 2
X49LTWR_gt 2 2
X49LTWRR_gt 3 1
X95LTWR_gt 2 2
X95LTWRR_gt 2 2
X99LTWR_gt 2 2dim(geno.numeric)[1] 10 134421dim(gts)[1] 134421 12get the principal components
pca <- prcomp(geno.numeric)
summary(pca)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 74.5081 71.2377 60.3610 52.0207 51.21882 49.17714
Proportion of Variance 0.2043 0.1868 0.1341 0.0996 0.09655 0.08901
Cumulative Proportion 0.2043 0.3911 0.5252 0.6248 0.72135 0.81036
PC7 PC8 PC9 PC10
Standard deviation 42.71583 41.3407 40.23355 1.004e-11
Proportion of Variance 0.06716 0.0629 0.05958 0.000e+00
Cumulative Proportion 0.87752 0.9404 1.00000 1.000e+00plot it
plot.data <- pca$x %>%
as.data.frame %>%
rownames_to_column("sample") %>%
mutate(response=str_extract(sample, "(LTR|LTWR)")) %>%
gather(key="component", value="value",PC2:PC9)
plots <- map(sort(unique(plot.data$component)), function(x) {
plot.data %>%
filter(component==x) %>%
ggplot(aes(x=PC1, y= value, label=sample, color=response)) +
geom_point() + ylab(x)
}
)
plots [[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
make plot of PC1 vs PC2 for paper
pc1pc2 <- pca$x %>%
as.data.frame() %>%
rownames_to_column("ID") %>%
select(ID, PC1, PC2) %>%
mutate(ID={str_replace(ID, "W", "N") %>%
str_replace("RR", "R2") %>%
str_remove_all("(X|_gt)") },
response=ifelse(str_detect(ID,"N"), "no recovery", "recovery"))
pc1pc2pc1pc2 %>%
ggplot(aes(x=PC1, y = PC2, label=ID, color=response)) +
geom_point() +
geom_text_repel(show.legend=FALSE, direction="y")
ggsave("../output/PCAw38.pdf")Saving 6.87 x 4.25 in image
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